Motif-based Graph Attention Network for Web Service Recommendation

Deep learning on graphs and especially graph convolutional networks (GCNs) have shown superior performance in collaborative filtering. Most GCNs have a message-passing architecture that enables nodes aggregate information from neighbours continuously through multiple layers. This also leads to a common issue called over-smoothing: as the number of layers increases, the node embeddings become similar and the performance of tasks such as link prediction degrades severely. In this work, we propose motif-based graph attention network for web service recommendation (MGSR) that alleviates the over-smoothing issue by incorporate network motifs in layer propagation. Extensive experiments show that our model outperforms state-of-the-art approaches.